Awesome

Scalable Multi-Temporal Remote Sensing Change Data Generation via Simulating Stochastic Change Process (ICCV 2023)

[Paper] [BibTeX]

Features

• Generative change modeling decouples the complex stochastic change process simulation to more tractable change event simulation and semantic change synthesis.
• Change generator, i.e., Changen, enables object change generation with controllable object property (e.g., scale, position, orientation), and change event.
• Our synthetic change data pre-training empowers the change detectors with better transferability and zero-shot prediction capability

News

• 2023/10, ChangeStar (1x96) and its checkpoints are released.
• 2023/07, This paper is accepted by ICCV 2023.

Catalog

• ChangeStar (1x96) based on ResNet-18 and MiT-B1
• Fine-tuned checkpoints

Installation

Install EVer:

pip install ever-beta

Requirements:

• PyTorch>=1.10

Getting Started

We provide an out-of-box way to use our models via torch.hub. API usage is shown below. I believe this must be the simplest API you have ever used.

a. Model Construction:

import torch

# 1. Choose it if you want to use the network architecture only.

# 1.1 load a ChangeStar (1x96) model based on ResNet-18 (R18) from scratch
model = torch.hub.load('Z-Zheng/Changen', 'changestar_1x96', backbone_name='r18', force_reload=True)

# 1.2 load a ChangeStar (1x96) model based on MiT-B1 (a Transformer backbone) from scratch
model = torch.hub.load('Z-Zheng/Changen', 'changestar_1x96', backbone_name='mitb1', force_reload=True)

# 2. Choose it if you want to explore a well-trained model.

# 2.1 load a ChangeStar (1x96) model based on ResNet-18 (R18)
# pretrained on Changen-90k, fine-tuned on LEVIR-CD train set.
model = torch.hub.load('Z-Zheng/Changen', 'changestar_1x96', backbone_name='r18',
               pretrained=True, dataset_name='levircd', force_reload=True)

# 2.2 load a ChangeStar (1x96) model based on ResNet-18 (R18)
# pretrained on Changen-90k, fine-tuned on S2Looking train set.
model = torch.hub.load('Z-Zheng/Changen', 'changestar_1x96', backbone_name='r18',
               pretrained=True, dataset_name='s2looking', force_reload=True)

# 2.3 load a ChangeStar (1x96) model based on MiT-B1
# pretrained on Changen-90k, fine-tuned on LEVIR-CD train set.
model = torch.hub.load('Z-Zheng/Changen', 'changestar_1x96', backbone_name='mitb1',
               pretrained=True, dataset_name='levircd', force_reload=True)

# 2.4 load a ChangeStar (1x96) model based on MiT-B1
# pretrained on Changen-90k, fine-tuned on S2Looking train set.
model = torch.hub.load('Z-Zheng/Changen', 'changestar_1x96', backbone_name='mitb1',
               pretrained=True, dataset_name='s2looking', force_reload=True)

b. Run the Model

import torch

t1_image = torch.rand(1, 3, 512, 512)  # [b, c, h, w]
t2_image = torch.rand(1, 3, 512, 512)  # [b, c, h, w]
bi_images = torch.cat([t1_image, t2_image], dim=1)  # [b, tc, h, w]

model = torch.hub.load(...)  # refer to Step. a

predictions = model(bi_images)
change_prob = predictions['change_prediction']  # [b, 1, h, w]

If you want to delve into the model implementation, check changestar_1x96.py

<a name="Citation"></a>Citation

If you use Changen-pretrained models in your research, we hope you can kindly cite the following papers:

@inproceedings{zheng2023changen,
  title={Scalable Multi-Temporal Remote Sensing Change Data Generation via Simulating Stochastic Change Process},
  author={Zheng, Zhuo and Tian, Shiqi and Ma, Ailong and Zhang, Liangpei and Zhong, Yanfei},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
  pages={21818--21827},
  year={2023}
}

@inproceedings{zheng2021change,
  title={Change is Everywhere: Single-Temporal Supervised Object Change Detection in Remote Sensing Imagery},
  author={Zheng, Zhuo and Ma, Ailong and Zhang, Liangpei and Zhong, Yanfei},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
  pages={15193--15202},
  year={2021}
}

@article{zheng2023farseg++,
  title={FarSeg++: Foreground-Aware Relation Network for Geospatial Object Segmentation in High Spatial Resolution Remote Sensing Imagery},
  author={Zheng, Zhuo and Zhong, Yanfei and Wang, Junjue and Ma, Ailong and Zhang, Liangpei},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
  year={2023},
  volume={45},
  number={11},
  pages={13715-13729},
  publisher={IEEE}
}

@inproceedings{zheng2020foreground,
  title={Foreground-Aware Relation Network for Geospatial Object Segmentation in High Spatial Resolution Remote Sensing Imagery},
  author={Zheng, Zhuo and Zhong, Yanfei and Wang, Junjue and Ma, Ailong},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={4096--4105},
  year={2020}
}

License

This code is released under the Apache License 2.0.

Copyright (c) Zhuo Zheng. All rights reserved.